SoroushVahidi/scidocs huggingface dataset:
iCONCUR: informed consent for clinical data and bio-sample use for research
(2016)

Background
Implementation of patient preferences for use of electronic health records for research has been traditionally limited to identifiable data. Tiered e-consent for use of de-identified data has traditionally been deemed unnecessary or impractical for implementation in clinical settings.


Methods
We developed a web-based tiered informed consent tool called informed consent for clinical data and bio-sample use for research (iCONCUR) that honors granular patient preferences for use of electronic health record data in research. We piloted this tool in 4 outpatient clinics of an academic medical center.


Results
Of patients offered access to iCONCUR, 394 agreed to participate in this study, among whom 126 patients accessed the website to modify their records according to data category and data recipient. The majority consented to share most of their data and specimens with researchers. Willingness to share was greater among participants from an Human Immunodeficiency Virus (HIV) clinic than those from internal medicine clinics. The number of items declined was higher for for-profit institution recipients. Overall, participants were most willing to share demographics and body measurements and least willing to share family history and financial data. Participants indicated that having granular choices for data sharing was appropriate, and that they liked being informed about who was using their data for what purposes, as well as about outcomes of the research.


Conclusion
This study suggests that a tiered electronic informed consent system is a workable solution that respects patient preferences, increases satisfaction, and does not significantly affect participation in research.
Automotive radome design - fishnet structure for 79 GHz
(2010)

Metamaterials are considered as an option for quasi-optical matching of automotive radar radomes, lowering transmission loss and minimizing reflections. This paper shows a fishnet structure design for the 79 GHz band which is suitable for this type of matching and which exhibits a negative index of refraction. The measured transmission loss is 0.9 dB at 79 GHz. A tolerance study concerning copper plating, substrate permittivity, oblique incidence, and polarization is shown. Quasi-optical measurements were done in the range of 60 – 90 GHz, which agree with simulated results.
Avatar-independent scripting for real-time gesture animation
(2015)

When animation of a humanoid figure is to be generated at run-time, instead of by replaying precomposed motion clips, some method is required of specifying the avatar’s movements in a form from which the required motion data can be automatically generated. This form must be of a more abstract nature than raw motion data: ideally, it should be independent of the particular avatar’s proportions, and both writable by hand and suitable for automatic generation from higher-level descriptions of the required actions. We describe here the development and implementation of such a scripting language for the particular area of sign languages of the deaf, called SiGML (Signing Gesture Markup Language), based on the existing HamNoSys notation for sign languages. We conclude by suggesting how this work may be extended to more general animation for interactive virtual reality applications.
Analyzing multiple configurations of a C program
(2005)

Preprocessor conditionals are heavily used in C programs since they allow the source code to be configured for different platforms or capabilities. However, preprocessor conditionals, as well as other preprocessor directives, are not part of the C language. They need to be evaluated and removed, and so a single configuration selected, before parsing can take place. Most analysis and program understanding tools run on this preprocessed version of the code so their results are based on a single configuration. This paper describes the approach of CRefactory, a refactoring tool for C programs. A refactoring tool cannot consider only a single configuration: changing the code for one configuration may break the rest of the code. CRefactory analyses the program for all possible configurations simultaneously. CRefactory also preserves preprocessor directives and integrates them in the internal representations. The paper also presents metrics from two case studies to show that CRefactory's program representation is practical.
The contribution of electrophysiology to functional connectivity mapping
(2013)

A powerful way to probe brain function is to assess the relationship between simultaneous changes in activity across different parts of the brain. In recent years, the temporal activity correlation between brain areas has frequently been taken as a measure of their functional connections. Evaluating 'functional connectivity' in this way is particularly popular in the fMRI community, but has also drawn interest among electrophysiologists. Like hemodynamic fluctuations observed with fMRI, electrophysiological signals display significant temporal fluctuations, even in the absence of a stimulus. These neural fluctuations exhibit a correlational structure over a wide range of spatial and temporal scales. Initial evidence suggests that certain aspects of this correlational structure bear a high correspondence to so-called functional networks defined using fMRI. The growing family of methods to study activity covariation, combined with the diverse neural mechanisms that contribute to the spontaneous fluctuations, has somewhat blurred the operational concept of functional connectivity. What is clear is that spontaneous activity is a conspicuous, energy-consuming feature of the brain. Given its prominence and its practical applications for the functional connectivity mapping of brain networks, it is of increasing importance that we understand its neural origins as well as its contribution to normal brain function.
Practical Backscatter Communication Systems for Battery-Free Internet of Things: A Tutorial and Survey of Recent Research
(2018)

Backscatter presents an emerging ultralow-power wireless communication paradigm. The ability to offer submilliwatt power consumption makes it a competitive core technology for Internet of Things (IoT) applications. In this article, we provide a tutorial of backscatter communication from the signal processing perspective as well as a survey of the recent research activities in this domain, primarily focusing on bistatic backscatter systems. We also discuss the unique real-world applications empowered by backscatter communication and identify open questions in this domain. We believe this article will shed light on the low-power wireless connectivity design toward building and deploying IoT services in the wild.
OF A TUNABLE STIFFNESS COMPOSITE LEG FOR DYNAMIC LOCOMOTION
(2009)

Passively compliant legs have been instrumental in the development of dynamically running legged robots. Having properly tuned leg springs is essential for stable, robust and energetically efficient running at high speeds. Recent simulation studies indicate that having variable stiffness legs, as animals do, can significantly improve the speed and stability of these robots in changing environmental conditions. However, to date, the mechanical complexities of designing usefully robust tunable passive compliance into legs has precluded their implementation on practical running robots. This paper describes a new design of a ”structurally controlled variable stiffness” leg for a hexapedal running robot. This new leg improves on previous designs’ performance and enables runtime modification of leg stiffness in a small, lightweight, and rugged package. Modeling and leg test experiments are presented that characterize the improvement in stiffness range, energy storage, and dynamic coupling properties of these legs. We conclude that this variable stiffness leg design is now ready for implementation and testing on a dynamical running robot.
An in-depth study of the promises and perils of mining GitHub
(2015)

With over 10 million git repositories, GitHub is becoming one of the most important sources of software artifacts on the Internet. Researchers mine the information stored in GitHub’s event logs to understand how its users employ the site to collaborate on software, but so far there have been no studies describing the quality and properties of the available GitHub data. We document the results of an empirical study aimed at understanding the characteristics of the repositories and users in GitHub; we see how users take advantage of GitHub’s main features and how their activity is tracked on GitHub and related datasets to point out misalignment between the real and mined data. Our results indicate that while GitHub is a rich source of data on software development, mining GitHub for research purposes should take various potential perils into consideration. For example, we show that the majority of the projects are personal and inactive, and that almost 40 % of all pull requests do not appear as merged even though they were. Also, approximately half of GitHub’s registered users do not have public activity, while the activity of GitHub users in repositories is not always easy to pinpoint. We use our identified perils to see if they can pose validity threats; we review selected papers from the MSR 2014 Mining Challenge and see if there are potential impacts to consider. We provide a set of recommendations for software engineering researchers on how to approach the data in GitHub.
Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning
(2018)

This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. However, most of the recent methods rely on supervised learning, which requires large amounts of manually annotated data. Thus, it is challenging for these methods to cope with the growing amount of medical images. This paper proposes a unified approach to unsupervised deep representation learning and clustering for segmentation. Our proposed method consists of two phases. In the first phase, we learn deep feature representations of training patches from a target image using joint unsupervised learning (JULE) that alternately clusters representations generated by a CNN and updates the CNN parameters using cluster labels as supervisory signals. We extend JULE to 3D medical images by utilizing 3D convolutions throughout the CNN architecture. In the second phase, we apply k-means to the deep representations from the trained CNN and then project cluster labels to the target image in order to obtain the fully segmented image. We evaluated our methods on three images of lung cancer specimens scanned with micro-computed tomography (micro-CT). The automatic segmentation of pathological regions in micro-CT could further contribute to the pathological examination process. Hence, we aim to automatically divide each image into the regions of invasive carcinoma, noninvasive carcinoma, and normal tissue. Our experiments show the potential abilities of unsupervised deep representation learning for medical image segmentation.
Optimal Multiserver Configuration for Profit Maximization in Cloud Computing
(2013)

As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes critically important. To maximize the profit, a service provider should understand both service charges and business costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an application environment, the configuration of a multiserver system, the service-level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low-quality service, the cost of renting, the cost of energy consumption, and a service provider's margin and profit. Our approach is to treat a multiserver system as an M/M/m queuing model, such that our optimization problem can be formulated and solved analytically. Two server speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. The probability density function of the waiting time of a newly arrived service request is derived. The expected service charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server size and the optimal server speed are demonstrated.
Overview on Additive Manufacturing Technologies
(2017)

This paper provides an overview on the main additive manufacturing/3D printing technologies suitable for many satellite applications and, in particular, radio-frequency components. In fact, nowadays they have become capable of producing complex net-shaped or nearly net-shaped parts in materials that can be directly used as functional parts, including polymers, metals, ceramics, and composites. These technologies represent the solution for low-volume, high-value, and highly complex parts and products.
Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach
(2004)

Learning from imbalanced data sets, where the number of examples of one (majority) class is much higher than the others, presents an important challenge to the machine learning community. Traditional machine learning algorithms may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we describe a new approach that combines boosting, an ensemble-based learning algorithm, with data generation to improve the predictive power of classifiers against imbalanced data sets consisting of two classes. In the DataBoost-IM method, hard examples from both the majority and minority classes are identified during execution of the boosting algorithm. Subsequently, the hard examples are used to separately generate synthetic examples for the majority and minority classes. The synthetic data are then added to the original training set, and the class distribution and the total weights of the different classes in the new training set are rebalanced. The DataBoost-IM method was evaluated, in terms of the F-measures, G-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. Our results are promising and show that the DataBoost-IM method compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set. Results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes.
Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms
(1994)

With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term “prototypes” refers to the reference instances used in a nearest neighbor computation — the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely on stochastic techniques to search the space of sets of prototypes and are simple to implement. The first is a Monte Carlo sampling algorithm; the second applies random mutation hill climbing. On four datasets we show that only three or four prototypes sufficed to give predictive accuracy equal or superior to a basic nearest neighbor algorithm whose run-time storage costs were approximately 10 to 200 times greater. We briefly investigate how random mutation hill climbing may be applied to select features and prototypes simultaneously. Finally, we explain the performance of the sampling algorithm on these datasets in terms of a statistical measure of the extent of clustering displayed by the target classes.
Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers
(2004)

Undoubtedly, Customer Relationship Management (CRM) has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer’s future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial
Cooperative Intersection Management: A Survey
(2016)

Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.
Sequential Mining: Patterns and Algorithms Analysis
(2013)

This paper presents and analysis the common existing sequential pattern mining algorithms. It presents a classifying study of sequential pattern-mining algorithms into five extensive classes. First, on the basis of Apriori-based algorithm, second on Breadth First Search-based strategy, third on Depth First Search strategy, fourth on sequential closed-pattern algorithm and five on the basis of incremental pattern mining algorithms. At the end, a comparative analysis is done on the basis of important key features supported by various algorithms. This study gives an enhancement in the understanding of the approaches of sequential
Microstrip-fed circular substrate integrated waveguide (SIW) cavity resonator and antenna
(2010)

Substrate integrated waveguide (SIW) cavity resonators are among the emerging group of SIW-based circuit components that are gaining popularity and increasingly employed in integrated microwave and mm-wave circuits. The SIW cavities offer a significantly enhanced performance in comparison to the previously available planar microwave resonators. The high quality factor of the waveguide-based cavity resonators enables designing microwave oscillators with very low phase noise as well as compact high-gain antennas [1–4]. The SIW-cavity-based antennas also show much promise for implementation of low-cost lightweight fully-integrated high-gain antennas that find application in ultra-light communication satellites, low payload spacecrafts, high-frequency radar, and sensors. In this paper, a circular SIW cavity resonator, which is fed by a microstrip and via probe, is presented. The microstrip feed is optimized to achieve a return loss of better than 20dB from both simulation and measurements. A resonance frequency of 16.79GHz and quality factor of 76.3 are determined from vector network analyzer (VNA) measurements. Due to the folded slot on the upper conductor layer, the resonator is an efficient cavity backed antenna. A maximum gain of over 7dB is measured in an anechoic chamber at the resonance frequency of 16.79 GHz.
Quality driven web services composition
(2003)

The process-driven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at design-time. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service.
Navigating the massive world of reddit: using backbone networks to map user interests in social media
(2015)

In the massive online worlds of social media, users frequently rely on organizing themselves around specific topics of interest to find and engage with like-minded people. However, navigating these massive worlds and finding topics of specific interest often proves difficult because the worlds are mostly organized haphazardly, leaving users to find relevant interests by word of mouth or using a basic search feature. Here, we report on a method using the backbone of a network to create a map of the primary topics of interest in any social network. To demonstrate the method, we build an interest map for the social news web site reddit and show how such a map could be used to navigate a social media world. Moreover, we analyze the network properties of the reddit social network and find that it has a scale-free, small-world, and modular community structure, much like other online social networks such as Facebook and Twitter. We suggest that the integration of interest maps into popular social media platforms will assist users in organizing themselves into more specific interest groups, which will help alleviate the overcrowding effect often observed in large online communities. Subjects Network Science and Online Social Networks, Visual Analytics